Adaptive multiobjective memetic optimization: algorithms and applications

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dc.contributor.authorDang, Hieu
dc.contributor.examiningcommitteeHossain, Ekram (Electrical and Computer Engineering) Irani, Pourang (Computer Science) Gumel, Abba (Mathematics) Oommen, B. John (Carleton University)en_US
dc.contributor.supervisorKinsner, Witold (Electrical and Computer Engineering)en_US
dc.date.accessioned2015-09-30T21:03:35Z
dc.date.available2015-09-30T21:03:35Z
dc.date.issued2012-08en_US
dc.date.issued2012-12en_US
dc.date.issued2014-03en_US
dc.date.issued2014-05en_US
dc.date.issued2014-12en_US
dc.date.issued2015-07en_US
dc.degree.disciplineElectrical and Computer Engineeringen_US
dc.degree.levelDoctor of Philosophy (Ph.D.)en_US
dc.description.abstractThe thesis presents research on multiobjective optimization based on memetic computing and its applications in engineering. We have introduced a framework for adaptive multiobjective memetic optimization algorithms (AMMOA) with an information theoretic criterion for guiding the selection, clustering, and local refinements. A robust stopping criterion for AMMOA has also been introduced to solve non-linear and large-scale optimization problems. The framework has been implemented for different benchmark test problems with remarkable results. This thesis also presents two applications of these algorithms. First, an optimal image data hiding technique has been formulated as a multiobjective optimization problem with conflicting objectives. In particular, trade-off factors in designing an optimal image data hiding are investigated to maximize the quality of watermarked images and the robustness of watermark. With the fixed size of a logo watermark, there is a conflict between these two objectives, thus a multiobjective optimization problem is introduced. We propose to use a hybrid between general regression neural networks (GRNN) and the adaptive multiobjective memetic optimization algorithm (AMMOA) to solve this challenging problem. This novel image data hiding approach has been implemented for many different test natural images with remarkable robustness and transparency of the embedded logo watermark. We also introduce a perceptual measure based on the relative Rényi information spectrum to evaluate the quality of watermarked images. The second application is the problem of joint spectrum sensing and power control optimization for a multichannel, multiple-user cognitive radio network. We investigated trade-off factors in designing efficient spectrum sensing techniques to maximize the throughput and minimize the interference. To maximize the throughput of secondary users and minimize the interference to primary users, we propose a joint determination of the sensing and transmission parameters of the secondary users, such as sensing times, decision threshold vectors, and power allocation vectors. There is a conflict between these two objectives, thus a multiobjective optimization problem is used again in the form of AMMOA. This algorithm learns to find optimal spectrum sensing times, decision threshold vectors, and power allocation vectors to maximize the averaged opportunistic throughput and minimize the averaged interference to the cognitive radio network.en_US
dc.description.noteFebruary 2016en_US
dc.identifier.citationHieu V. Dang and Witold Kinsner, "Multiobjective multivariate optimization of joint spectrum sensing and power control in cognitive wireless networks," International Journal of Cognitive Informatics and Natural Intelligence (Accepted for publication, Aug. 2015).en_US
dc.identifier.citationHieu V. Dang, Witold Kinsner, and YingxuWang, "Multiobjective image data hiding based on neural networks and memetic optimization," WSEAS Trans. Signal Processing, vol. 10, pp. 645-661, Dec. 2014.en_US
dc.identifier.citationHieu V. Dang and Witold Kinsner, "A perceptual data hiding mathematical model for color image protection," Journal of Advanced Mathematics and Applications, vol. 1, no. 2, pp. 218-233, Dec. 2012.en_US
dc.identifier.citationHieu V. Dang and Witold Kinsner, "An analytical multiobjective optimization of joint spectrum sensing and power control in cognitive radio networks," in Proc. of the 14th IEEE Intern. Conf. on Cognitive Informatics and Cognitive Computing, ICCI*CC 2015, (Beijing, China: July 6-8, 2015), pp. 39-48, 2015.en_US
dc.identifier.citationHieu V. Dang and Witold Kinsner, "A multiobjective memetic optimization for spectrum sensing and power allocation in cognitive wireless networks," in Proc. of the IEEE Canadian Conf. on Electrical and Computer Engineering, CCECE 2014, (Toronto, Canada: May 4.7), pp. 1-6 , 2014.en_US
dc.identifier.citationHieu V. Dang and Witold Kinsner, "Optimal colour image watermarking using neural networks and multiobjective memetic optimization," in Proc. of the 2014 Intern. Conf. on Neural Networks and Fuzzy Systems, ICNN-FS 2014, (Venice, Italy; March 15-17, 2014), pp. 63-74, 2014.en_US
dc.identifier.citationHieu V. Dang and Witold Kinsner, "An intelligent digital color image watermarking approach based on wavelet transform and general regression neural network," in Proc. of the 11th IEEE Intern. Conf. on Cognitive Informatics and Cognitive Computing, ICCI*CC 2012, (Kyoto, Japan: August 22-24, 2012), pp. 115-123, 2012.en_US
dc.identifier.urihttp://hdl.handle.net/1993/30856
dc.language.isoengen_US
dc.publisherJournal of Cognitive Informatics and Natural Intelligenceen_US
dc.publisherWSEAS Transactions on Signal Processingen_US
dc.publisherJournal of Advanced Mathematics and Applicationsen_US
dc.publisherIEEE International Conference on Cognitive Informatics and Cognitive Computingen_US
dc.publisherIEEE Canadian Conference on Electrical and Computer Engineeringen_US
dc.publisherInternational Conference on Neural Networks and Fuzzy Systemsen_US
dc.rightsopen accessen_US
dc.subjectMultiobjective optimizationen_US
dc.subjectMemetic computingen_US
dc.subjectInformation hidingen_US
dc.subjectImage watermarkingen_US
dc.subjectSpectrum sensingen_US
dc.subjectComputational intelligenceen_US
dc.subjectCognitive radio networksen_US
dc.subjectMultiscale information measuresen_US
dc.subjectPerceptual measuresen_US
dc.subjectNeural networksen_US
dc.subjectPower allocationen_US
dc.subjectWaveletsen_US
dc.subjectHuman visual systemsen_US
dc.titleAdaptive multiobjective memetic optimization: algorithms and applicationsen_US
dc.typedoctoral thesisen_US
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